nanoresearch-experimen…
Generate a Python code skeleton from an experiment blueprint
Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
$ npx -y skills add OpenRaiser/NanoResearch --skill unsloth --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/unslothContext preview
The summary Claude sees to decide when to auto-load this skill.
Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
name: unsloth description: Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization version: 1.0.0 author: Orchestra Research license: MIT tags: [Fine-Tuning, Unsloth, Fast Training, LoRA, QLoRA, Memory-Efficient, Optimization, Llama, Mistral, Gemma, Qwen] dependencies: [unsloth, torch, transformers, trl, datasets, peft]
Comprehensive assistance with unsloth development, generated from official documentation.
This skill should be triggered when:
*Quick reference patterns will be added as you use the skill.*
This skill includes comprehensive documentation in `references/`:
Use `view` to read specific reference files when detailed information is needed.
Start with the getting_started or tutorials reference files for foundational concepts.
Use the appropriate category reference file (api, guides, etc.) for detailed information.
The quick reference section above contains common patterns extracted from the official docs.
Organized documentation extracted from official sources. These files contain:
Add helper scripts here for common automation tasks.
Add templates, boilerplate, or example projects here.
To refresh this skill with updated documentation: 1. Re-run the scraper with the same configuration 2. The skill will be rebuilt with the latest information
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端到端自主 AI 科研引擎 — 从研究想法到完整论文,全程自动化 快速开始 · 效果展示 · 流水线 · Claude Code · 飞书机器人 🔬 NanoResearch 真正运行计算实验——它不仅生成代码,还能将代码提交到 GPU 集群执行训练,收集真实实验结果,生成论文配图,最终输出一篇有实验数据支撑的完整 LaTeX 论文。论文中的每一个数据、表格、图表都来自实际运行的实验结果,而非 LLM 编造。
Generate a Python code skeleton from an experiment blueprint
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Produce an experiment blueprint from a research hypothesis
Draft a LaTeX research paper from all previous stage outputs
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